A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 9023556 |
| Pages (from-to) | 36-48 |
| Journal / Publication | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 32 |
| Issue number | 1 |
| Online published | 3 Mar 2020 |
| Publication status | Published - Jan 2021 |
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Abstract
This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at two timescales. In addition, particle swarm optimization is used to update the initial neuronal states iteratively to escape from local minima toward better initial states. In spite of its minimal system complexity, the approach is proven to be almost surely convergent to optimal solutions. Its superior performance is substantiated via solving five benchmark problems.
Research Area(s)
- Almost-sure convergence, mixed-integer optimization, neural networks
Citation Format(s)
A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization. / Che, Hangjun; Wang, Jun.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 1, 9023556, 01.2021, p. 36-48.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
